The demand for data services has steadily increased putting continuous pressure on data service providers to increase the data throughput of their networks. CATV networks are governed by a set of DOCSIS standards that place hard limits on bandwidth and data rates. The latest version of the DOCSIS standard, DOCSIS 3.1 was released in October 2013. DOCSIS 3.1 increases the bandwidth and data throughput available in CATV networks by up to 10 Gbps downstream and 1 Gbps upstream.
DOCSIS 3.1 is markedly different from prior versions of the standard in that Orthogonal Frequency Division Multiple Access (OFDMA) is used in both the upstream and downstream directions. By effectively modulating signals on narrow-band carriers, OFDM can mitigate inter-symbol interference while simplifying the structure of the channel equalizer.
While OFDMA systems have been studied for many years, the DOCSIS 3.1 standard is unique in that it combines OFDMA with very high-order modulation schemes (up to 4096-QAM). Furthermore, cable plants generate a number of channel impairments, some of which differ from typical OFDMA systems discussed in the literature.
In order to effectively demodulate a spectrally efficient signal, it is necessary to employ coherent demodulation which involves estimation and tracking of the multipath channel. To aid in channel estimation, DOCSIS 3.1 specifies a pilot-based wide-band probing mode, where the sub-carriers of an OFDM symbol are dedicated to channel estimation.
There are several channel estimation techniques that have been studied for pilot-based estimation. The simplest one, which is Least Square (LS) estimation, as set out in Document 1 below, does not require any channel state information (CSI). LS estimators work with samples in the frequency domain and are relatively low in complexity. However, they suffer from relatively high mean-square error, which is proportional to the power of additive white Gaussian noise (AWGN).
A better technique, which also performs estimation in the frequency domain, is linear minimum mean-square error (LMMSE) estimation in the Document 2 below. This technique yields much better performance than the LS estimator, especially under low signal-to-noise ratio (SNR) scenarios. The major drawback of the LMMSE estimator is that it requires knowledge of the channel auto-correlation matrix and the noise variance, which are usually unknown at the receiver. The computational complexity of the LMMSE estimator is also very high as it requires a matrix inversion. Many have attempted to reduce the complexity of the LMMSE estimator (as set out in Documents 3 and 4 below) at the expense of a small sacrifice in estimation accuracy.
Another very good approach uses discrete Fourier transform (DFT) based channel estimation. The DFT-based method firstly employs an LS estimator to obtain the channel's frequency response (CFR). Then the discrete-time channel impulse response (CIR) is obtained by performing an inverse discrete Fourier transform (IDFT) on the CFR. Since the energy of the CIR is typically concentrated in a few taps having short delays, the algorithm's performance can be improved if a few taps whose power is significantly higher than noise are preserved while the rest are forced to zero (as set out in Document 5 below). This operation is commonly referred to as denoising. After denoising, the CIR is transformed back to the frequency domain to obtain the estimated CFR. Consequently, the DFT approach helps to remove the noise power from the LS-estimated CFR. In general, DFT-based methods have moderate complexity thanks to Fast Fourier Fransform (FFT) algorithms and perform much better than the LS estimator at low SNRs (as set out in Document 6 below).
However, with the DFT method, performance degradation can occur due to leakage between samples in the discrete-time CIR. There are two sources of leakage. The first is leakage by multipath components that have non sample-spaced delays. In the case of non sample-spaced delays, the energy from a single multipath component is spread over multiple sample-spaced taps in the discrete time CIR. When the noise-only taps are eliminated, portions of the leakage energy are also removed and thus the estimation will show an error floor. The second type of leakage emerges if not all sub-carriers are used for channel estimation. In particular, in a typical OFDM system, the sub-carriers at both ends of the spectrum are left null to form guard bands. Not using the end sub-carriers degrades the performance of DFT-based techniques as this is equivalent to placing a rectangular window in the frequency domain which translates to convolution with a sinc-like function in the time domain. This causes the energy of the CIR to spread out in time. Denoising cuts off the tails of the sinc-like functions causing ripples around the edge sub-carriers when the denoised CIR is converted back to the frequency domain (as set out in Document 7 below). This phenomenon is often referred to as an “edge effect” or “border effect” and results in estimation errors not being equally distributed over all sub-carriers. To date studies that effectively address the two leakage issues of the DFT-based techniques have not been found.
The usefulness of the standard channel estimation techniques discussed above is somewhat limited in DOCSIS 3.1 systems, as upstream wideband probing has a subcarrier skipping option. In subcarrier skipping mode, multiple upstream users transmit wideband probing signals on different subcarriers of the same OFDM symbol. Each user transmits on a different set of subcarriers that are spaced K sub-carriers apart, where K is the number of simultaneous users. The use of subcarrier skipping with K simultaneous users allows a K-fold increase in the efficiency of the wideband probing process as compared to a single user probing scheme. However, it places additional computational burden on the receiver, which must generate an estimate of the entire channel for each user despite receiving pilots on only every K'th subcarrier.
The following documents provide further information on this subject:    [1] Y. Shen and E. Martinez, “Channel Estimation in OFDM Systems,” Freescale Semiconductor Application Note, 2006.    [2] J.-J. van de Beek, O. Edfors, M. Sandell, S. Wilson, and P. Ola Borjesson, “On channel estimation in OFDM systems,” in Proc. IEEE Veh. Technol. Conf, vol. 2, July 1995, pp. 815-819.    [3] O. Edfors, M. Sandell, J.-J. van de Beek, S. Wilson, and P. Borjesson, “OFDM channel estimation by singular value decomposition,” IEEE Trans. Commun., vol. 46, no. 7, pp. 931-939, July 1998.    [4] M. Noh, Y. Lee, and H. Park, “Low complexity LMMSE channel estimation for OFDM,” IEE Proc. Commun., vol. 153, no. 5, pp. 645-650, October 2006.    [5] Y. Zhao and A. Huang, “A novel channel estimation method for OFDM mobile communication systems based on pilot signals and transform-domain processing,” in Proc. IEEE Veh. Technol. Conf., vol. 3, May 1997, pp. 2089-2093.    [6] Y. Kang, K. Kim, and H. Park, “Efficient DFT-based channel estimation for OFDM systems on multipath channels,” IET Commun., vol. 1, no. 2, pp. 197-202, April 2007.    [7] M. Ozdemir and H. Arslan, “Channel Estimation for Wireless OFDM Systems,” IEEE Commun. Surveys Tutorials, vol. 9, no. 2, pp. 18-48, July 2007.    [8] Cable Television Laboratories, Inc., “DOCSIS 3.1 Physical Layer Specification,” October 2013.    [9] M. Morelli, “Timing and Frequency Synchronization for the Uplink of an OFDMA System,” IEEE Trans. Commun., vol. 52, no. 1, pp. 166-166, March 2004.
All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. The disclosures in the above documents can be considered for further details of any matters not fully discussed herein.